# Types The completion types used by `any_llm.completion()` and `any_llm.acompletion()` are re-exports from the [OpenAI Python SDK](https://github.com/openai/openai-python), extended where needed to support additional fields like reasoning content. ### Primary Types #### `ChatCompletion` The response object for a non-streaming completion request. Extends `openai.types.chat.ChatCompletion` with support for reasoning content in the message choices. **Import:** `from any_llm.types.completion import ChatCompletion` Key fields: | Field | Type | Description | | -------------- | -------------- | ----------- | | `choices` | `list[Choice]` | | | `service_tier` | `str \| None` | | #### `ChatCompletionChunk` A single chunk in a streaming completion response. Extends `openai.types.chat.ChatCompletionChunk`. **Import:** `from any_llm.types.completion import ChatCompletionChunk` Key fields: | Field | Type | Description | | --------- | ------------------- | ----------------------------------------------------------------------------------------------- | | `id` | `str` | Completion identifier (same across all chunks). | | `choices` | `list[ChunkChoice]` | Each chunk choice has a `delta` with incremental `content`, `role`, and optionally `reasoning`. | | `model` | `str` | The model used. | #### `ChatCompletionMessage` A message within a completion response. Extends `openai.types.chat.ChatCompletionMessage` with a `reasoning` field. **Import:** `from any_llm.types.completion import ChatCompletionMessage` | Field | Type | Description | | ------------- | --------------------------------------------- | -------------------------------------------------------- | | `role` | `str` | Message role (e.g., `"assistant"`). | | `content` | `str \| None` | Text content of the message. | | `reasoning` | `Reasoning \| None` | Reasoning/thinking content (when the model supports it). | | `tool_calls` | `list[ChatCompletionMessageToolCall] \| None` | Tool calls requested by the model. | | `annotations` | `list[dict] \| None` | Annotations attached to the message. | #### `ParsedChatCompletion` Returned when `response_format` is a Pydantic `BaseModel` subclass or a dataclass type. Extends `ChatCompletion` with a generic type parameter. **Import:** `from any_llm import ParsedChatCompletion` Access the parsed object via `response.choices[0].message.parsed`, which will be an instance of the type passed as `response_format`. #### `CreateEmbeddingResponse` Response object for embedding requests. Re-exported directly from `openai.types.CreateEmbeddingResponse`. **Import:** `from any_llm.types.completion import CreateEmbeddingResponse` | Field | Type | Description | | ------- | ----------------- | ----------------------------------------------------------------------- | | `data` | `list[Embedding]` | List of embedding objects, each with an `embedding` vector and `index`. | | `model` | `str` | The model used. | | `usage` | `Usage` | Token usage with `prompt_tokens` and `total_tokens`. | #### `ReasoningEffort` A literal type controlling reasoning depth for models that support it. **Import:** `from any_llm.types.completion import ReasoningEffort` ``` ReasoningEffort = Literal["none", "minimal", "low", "medium", "high", "xhigh", "auto"] ``` The value `"auto"` (the default) maps to each provider's own default reasoning level. ### Internal Types #### `CompletionParams` Normalized parameters for chat completions, used internally to pass structured parameters from the public API to provider implementations. **Import:** `from any_llm.types.completion import CompletionParams` | Field | Type | Description | | ----------------------- | ------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------- | | `model_id` | `str` | Model identifier (e.g., 'mistral-small-latest') | | `messages` | `list[dict[str, Any]]` | List of messages for the conversation | | `tools` | `list[dict[str, Any] \| Any] \| None` | List of tools for tool calling. Should be converted to OpenAI tool format dicts | | `tool_choice` | `str \| dict[str, Any] \| None` | Controls which tools the model can call | | `temperature` | `float \| None` | Controls randomness in the response (0.0 to 2.0) | | `top_p` | `float \| None` | Controls diversity via nucleus sampling (0.0 to 1.0) | | `max_tokens` | `int \| None` | Maximum number of tokens to generate | | `response_format` | `dict[str, Any] \| type \| None` | Format specification for the response. Accepts Pydantic BaseModel subclasses, dataclass types, or dicts. | | `stream` | `bool \| None` | Whether to stream the response | | `n` | `int \| None` | Number of completions to generate | | `stop` | `str \| list[str] \| None` | Stop sequences for generation | | `presence_penalty` | `float \| None` | Penalize new tokens based on presence in text | | `frequency_penalty` | `float \| None` | Penalize new tokens based on frequency in text | | `seed` | `int \| None` | Random seed for reproducible results | | `user` | `str \| None` | Unique identifier for the end user | | `parallel_tool_calls` | `bool \| None` | Whether to allow parallel tool calls | | `logprobs` | `bool \| None` | Include token-level log probabilities in the response | | `top_logprobs` | `int \| None` | Number of top alternatives to return when logprobs are requested | | `logit_bias` | `dict[str, float] \| None` | Bias the likelihood of specified tokens during generation | | `stream_options` | `dict[str, Any] \| None` | Additional options controlling streaming behavior | | `max_completion_tokens` | `int \| None` | Maximum number of tokens for the completion (provider-dependent) | | `reasoning_effort` | `Literal['none', 'minimal', 'low', 'medium', 'high', 'xhigh', 'auto'] \| None` | | ### Additional Re-exports The following types are also available from `any_llm.types.completion`: | Type | Origin | Description | | --------------------- | ------------------------------ | --------------------------------------- | | `CompletionUsage` | `openai.types.CompletionUsage` | Token usage counts. | | `Function` | `openai.types.chat` | Function definition within a tool call. | | `Embedding` | `openai.types.Embedding` | Single embedding vector with index. | | `ChoiceDeltaToolCall` | `openai.types.chat` | Tool call delta in streaming chunks. | For full field-level documentation of the base OpenAI types, see the [OpenAI Python SDK reference](https://github.com/openai/openai-python). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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